Bayesian Model Selection Approach for Parsimonious Gaussian Mixture Models


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Documentation for package ‘bpgmm’ version 1.0.7

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CalculateProposalLambda CalculateProposalLambda
CalculateProposalPsy CalculateProposalPsy
calculateRatio Log scale ratio calculation
calculateVarList calculateVarList
changeConstraintFormat changeConstraintFormat
clearCurrentThetaYlist clearCurrentThetaYlist
combineClusterPara combineClusterPara
evaluatePrior evaluate Prior
evaluatePriorLambda evaluatePriorLambda
evaluatePriorPsi evaluatePriorPsi
EvaluateProposalLambda EvaluateProposalLambda
EvaluateProposalPsy EvaluateProposalPsy
generatePriorLambda generatePriorLambda
generatePriorPsi generatePriorPsi
generatePriorThetaY PriorThetaY list
getIndThetaY getIndThetaY
getmode getmode
getRemovedIndThetaY getRemovedIndThetaY
getThetaYWithEmpty getThetaYWithEmpty
getZmat Tool for vector to matrix
Hparam-class An S4 class to represent a Hyper parameter.
likelihood likelihood
listToStrVec Convert list of string to vector of string
MstepRJMCMCupdate MstepRJMCMCupdate
pgmmRJMCMC bpgmm Model-Based Clustering Using Baysian PGMM Carries out model-based clustering using parsimonious Gaussian mixture models. MCMC are used for parameter estimation. The RJMCMC is used for model selection.
stayMCMCupdate stayMCMCupdate
sumerizeZ sumerizeZ
summerizePgmmRJMCMC summerizePgmmRJMCMC
ThetaYList ThetaYList-class
toEthetaYlist Title
toNEthetaYlist toNEthetaYlist
updatePostThetaY Update posterior theta Y list
updatePostZ updatePostZ
VstepRJMCMCupdate VstepRJMCMCupdate